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11,105
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11,105 results for “single cells”
Single-cell RNA-Seq and TCR-Seq analysis of PD-1+ CD8+ T-cells responding to anti-PD-1 and anti-PD-1/CTLA-4 immunotherapy in melanoma
<p><strong>This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference) </strong></p> <p> </p> <p><strong>*experimental design</strong></p> <p> Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*extract protocol</strong></p> <p> PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.</p> <p> </p> <p><strong>*library construction protocol</strong></p> <p> Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.</p> <p> </p> <p><strong>*library strategy</strong></p> <p> scRNA-seq and scTCR-seq</p> <p> </p> <p><strong>*data processing step</strong></p> <p> Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.</p> <p> Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).</p> <p> </p> <p> <strong>*genome build/assembly</strong></p> <p> Alignment was performed using prebuilt Cell Ranger human reference GRCh38.</p> <p> </p> <p><strong>*processed data files format and content</strong></p> <p> RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.</p> <p>Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>file name</strong></p> </td> <td> <p><strong>file checksum</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>da2e006d2b39485fd8cf8701742c6d77</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>e125fc5031899bba71e1171888d78205</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>927241805d507204fbe9ef7045d0ccf4</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>8ca544d27f06e66592b567d3ab86551e</p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p><strong>*processed data file </strong></p> </td> <td> <p><strong>antibodies/tags</strong></p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M1_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M1_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - C1_base_combined_therapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - C1_post_combined_therapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C2_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C2_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_160421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>none</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_feature_bc_matrix.zip</p> </td> <td> <p>TotalSeq™-C0251 anti-human Hashtag 1 Antibody - (HASH_1) - M2_base_monotherapy<br>TotalSeq™-C0252 anti-human Hashtag 2 Antibody - (HASH_2) - M2_post_monotherapy<br>TotalSeq™-C0253 anti-human Hashtag 3 Antibody - (HASH_3) - M3_base_monotherapy<br>TotalSeq™-C0254 anti-human Hashtag 4 Antibody - (HASH_4) - M3_post_monotherapy<br>TotalSeq™-C0255 anti-human Hashtag 5 Antibody - (HASH_5) - C3_base_combined_therapy<br>TotalSeq™-C0256 anti-human Hashtag 6 Antibody - (HASH_6) - C3_post_combined_therapy</p> </td> </tr> <tr> <td> <p>PD1CD8_190421_filtered_contig_annotations.csv</p> </td> <td> <p>none</p> </td> </tr> </tbody> </table> <p> </p>
Encompassing view of spatial and single-cell RNA-seq renews the role of the microvasculature in human atherosclerosis
<p>Here we generate an integrative high-resolution map of human atherosclerotic plaques combining single-cell RNA-seq from multiple studies and spatial transcriptomics data from 12 human specimens, with different stages of atherosclerosis. We show cell-type and atherosclerosis-specific expression changes and spatially constrained alterations in cell-cell communication.</p>
Resolving Organoid Brain Region Identities by Mapping Single-Cell Genomic Data to Reference Atlases
<p>Data underlying the figures in the publication “Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases”, published in <em>Cell Stem Cell, </em><strong>2021</strong><em>, </em>28, 1148–1159.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1934590921000655">https://www.sciencedirect.com/science/article/pii/S1934590921000655</a></p> <p>Table of contents:</p> <p><strong>1. patscreen_srt.rds</strong>; Numerical data for <em>Figure 7</em>: RNA-seq data of a patterning screen in organoids with an array of small molecules. The dataset is in the rds data format, which can be opened in the R programming language using the function `readRDS()`. Once opened, the dataset is a Seurat object (https://satijalab.org/seurat/) and contains both the transcript counts and the metadata for all samples in the screen. The raw data used in figure 7 was also deposited in ArrayExpress (<a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10037/</a>)</p>
Data from: Identifying extracellular vesicles from single cells
<p>Extracellular vesicles (EVs) are constantly secreted from both eukaryotic and prokaryotic cells. EVs, including those referred to as exosomes, may have an impact on cell signaling and an incidence in diseased cells. In this manuscript, a platform to capture, quantify, and phenotypically classify the EVs secreted from single cells is introduced. Microfluidic chambers of about 300 pL are employed to trap and isolate individual cells. The EVs secreted within these chambers are then captured by surface-immobilized monoclonal antibodies (mAbs), irrespectively of their intracellular origin. Immunostaining against both plasma-membrane and cytosolic proteins was combined with highly sensitive, multi-color total internal reflection fluorescence microscopy (TIRFM) to characterize the immobilized vesicles. The data analysis of high-resolution images allowed the assignment of each detected EV to one of 15 unique populations, and demonstrated the presence of highly-heterogeneous phenotypes even at the single-cell level. The analysis also revealed that each mAb isolates phenotypically-different EVs, and more vesicles were effectively immobilized when CD63 was targeted instead of CD81. Finally, we demonstrate how an heterogeneous suppression in the secreted vesicles is obtained when the enzyme neutral sphingomyelinase is inhibited.</p>
Improving the efficiency of single cell genome sequencing based on overlapping pooling strategy
Single cell genome sequencing has become a useful tool in medicine and biology studies. However, an independent library is required for each cell in single cell genome sequencing, so that the cost grows in step with the number of cells. In this study, we report a study on efficient single-cell copy number variation (CNV) analysis based on overlapping pooling strategy together with branch and bound (B&B) algorithm. Single cells are overlapped pooled before sequencing, and later are assorted into specific types by estimating their CNV patterns by B&B algorithm. Instead of constructing libraries for each cell, a library is required only for each pool. As long as the number of pools is smaller than the cells, fewer libraries are needed, and a lower cost is spent. Through computer simulations, we overlapping pooled 80 cells into 40 and 27 pools and classified them into cell types based on CNV pattern. The results showed that 84% cells in 40 pools and 76.5% cells in 27 pools were correctly classified on average, while only half or one-third of the sequencing libraries are required. Combining with traditional approaches, our method is expected to significantly improve the efficiency of single cell genome sequencing.
Single-cell profiling of human bone marrow progenitors reveals mechanisms of failing erythropoiesis in Diamond-Blackfan anemia
<p>Ribosome dysfunction underlies the pathogenesis of many cancers and heritable ribosomopathies. Here we investigate how mutations in either ribosomal protein large (RPL) or ribosomal protein small (RPS) subunit genes selectively affect erythroid progenitor development and clinical phenotypes in Diamond-Blackfan anemia (DBA), a rare ribosomopathy with limited therapeutic options. Using single-cell assays of patient-derived bone marrow, we delineated two distinct cellular trajectories segregating with ribosomal protein genotypes: almost complete loss of erythroid specification were observed in <em>RPS</em>-DBA. In contrast, we observed relative preservation of qualitatively abnormal erythroid progenitors and precursors in <em>RPL</em>-DBA. Although both DBA genotypes exhibited a pro-inflammatory bone marrow milieu, <em>RPS</em>-DBA was characterized by erythroid differentiation arrest, whereas <em>RPL</em>-DBA was characterized by preserved GATA1 expression and activity. Compensatory stress erythropoiesis in <em>RPL</em>-DBA exhibited disordered differentiation underpinned by an altered glucocorticoid molecular signature, including reduced <em>ZFP36L2</em> expression<em>,</em> leading to milder anemia and improved corticosteroid response. This integrative analysis approach identified distinct pathways of erythroid failure and defined genotype-phenotype correlations in DBA. These findings may help facilitate therapeutic target discovery.</p> <p> </p>
Single cell ATAC-seq Mouse Kidney Data: Chromatin-accessibility estimation from single-cell ATAC data with scOpen
<p>We provide results regarding the bioinformatic analysis of scATAC-seq from mouse UUO kidney at different time points.</p>
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
<p>Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.</p>
Sampling time-dependent artifacts in single-cell genomics studies: scRNA-seq data
<p>Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.</p> <p>This repository contains the expression matrices and Seurat objects associated with the scRNA-seq data of the manuscript: "Sampling time-dependent artifacts in single-cell genomics studies" published in Genome Biology in 2020. The purpose of this repo is to share processed files and metadata for immediate access and reproducibility. The code to analyze it is thoroughly documented at the associated Github repository (https://github.com/massonix/sampling_artifacts).</p>
single-cell RNAseq data (data set 20) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset20) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from breast cancer samples downloaded from the GEO website (GSE180286)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 18) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset18) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 1 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 17) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset17 was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from PBMC metastatic MCC samples downloaded from the GEO website (GSE117988)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 12) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset12) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor10 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 16) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset16) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD4 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <p> </p>
single-cell RNAseq data (data set 11) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset11) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor9 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 14) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset14) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor12 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 9) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset9) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor7 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 8) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset8) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor6 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 7) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset7) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor5 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 19) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset19) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from Liver cancer set 2 samples downloaded from the GEO website (GSE125449)<strong>. </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.